AWS Data Wrangler

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.

It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.

It enable users to ingest, blend, cleanse and prepare diverse data from any source. With visual tools to eliminate coding and complexity, It puts the best quality data at the fingertips of IT and the business.

It is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.

An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

It is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.

It is an open source, visual language for data science that lets you design, prototype and develop any application by connecting visual elements together. Build dashboards, RPA workflows, and apps. No coding required.

It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.

It is the platform democratizing access to data and enabling enterprises to build their own path to AI in a human-centric way.

It is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. It was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.

It is a new visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning. You can choose from over 250 pre-built transformations to automate data preparation tasks, all without the need to write any code. You can automate filtering anomalies, converting data to standard formats, and correcting invalid values, and other tasks. After your data is ready, you can immediately use it for analytics and machine learning projects. You only pay for what you use - no upfront commitment.

Machine learners share, stress test, and stay up-to-date on all the latest ML techniques and technologies. Discover a huge repository of community-published models, data & code for your next project.

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

It is an open-source matrix library accelerated with NVIDIA CUDA. CuPy provides GPU accelerated computing with Python. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture.

It is a Google Chrome extension that helps you scrape data from web pages and into a CSV file or Excel spreadsheet.

It is data modeling tool used to find, visualize, design, deploy and standardize high-quality enterprise data assets. Discover and document any data from anywhere for consistency, clarity and artifact reuse across large-scale data integration, master data management, metadata management, Big Data, business intelligence and analytics initiatives – all while supporting data governance and intelligence efforts.

It is a tool that lets you and your team easily share knowledge and explore data. With the API you can send timeseries data to Clarify and use timelines to visualize and collaborate around this data.

It is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories.

It is a data science platform for tracking experiments, versioning data, models, and pipelines, using Git. It allows your whole team to compare, reproduce, and contribute to each other's work. It allows your whole team to compare, reproduce, and contribute to each other's work.

It is an advanced data warehouse and analytics platform available both on premises and on cloud. With enhancements to in-database analytics capabilities, this next generation of Netezza enables you to do data science and machine learning with data volumes scaling into the petabytes.

It is a better place for your data science projects, Jupyter notebooks, machine learning models, experiment logs, results, and more.

It is an open-source, Kubernetes-native workflow orchestrator implemented in Go. It enables highly concurrent, scalable, and reproducible workflows for data processing, machine learning and analytics.

It is a next-generation data analytics and business intelligence platform that excels at rapidly delivering business value from transactional data and is the first real breakthrough in data analytics in 20 years. It provides an integrated end-to-end data experience, from data acquisition and enrichment to visualizing and sharing results. It cuts project implementation time from months to weeks, provides revolutionary query speed, and maintains a unified, single-source of truth for multiple workloads including business intelligence, analytics, and machine learning.

It empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, It is an intelligent document and digital data integration platform. It combines patented and sophisticated image processing, capture technology, machine learning, and natural language processing.

It is a web-based data science tool that works on top of your filesystem allowing you to use your editor of choice. With Orchest you get to focus on visually building and iterating on your pipeline ideas. Under the hood Orchest runs a collection of containers to provide a scalable platform that can run on your laptop as well as on a large scale cloud cluster.

It is a leading provider in the specialized market of Enterprise Output Management. i-DOCS develops products and offers services that handle big volumes of sensitive data, automate business processes, deliver multi-channel communications, serve, store, archive data and documents.

It is a framework built on the top of Airflow that enables data scientists to create materialized views. It allows data scientists to focus on the logic of the view creation in their preferred tool (e.g., SQL, Python).

It is a visual data computing platform that provides a faster and more collaborative approach to take action through data. It provides a fast and collaborative way to explore data, create predictions, and deploy data apps.

It is an open-source data-centric IDE for NLP. Combining (semi-)automated labeling, extensive data management and neural search capabilities. It is like the data-centric sibling of your favorite programming environment.

It is a spreadsheet that lives inside your JupyterLab notebooks. It allows you to edit Pandas dataframes like an Excel file, and generates Python code that corresponds to each of your edits.

It is a web application to display time series data from various sources such as collectd, Graphite, InfluxDB or KairosDB on graphs, designed to be easy to setup and to use.

It is the platform to explore your data, develop and deploy your Python supercharged Notebooks and track the quality of your data using Machine learning.

It is a data solution and technology company that brings in NextGen solutions. We are specialized in data aggregation, preparation, and integration services, to deliver relevant data to downstream systems. Even customized solutions are provider to solve unique data/business challenges.

It is an open-source, offline browser-based tool for fast and intuitive data exploration and visualization. It can handle large data files, runs locally in your browser, and requires no backend setup.

It is a user-centered Python package for differentiable probabilistic inference. It allows to design and train differentiable Bayesian models using stochastic variational inference. It is based on the deep learning framework PyTorch.

It is the fastest way to transform text from chats, emails, surveys, reviews, social networks into real business intelligence. Experience the power of data science without being a data scientist